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Cited by 73Recommender Systems and TechniquesInformation Retrieval and Search BehaviorInformation Retrieval and Data Mining

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Scholarly recommendation systems: a literature survey

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Zhang, Zitong, Patra, Braja Gopal, Yaseen, Ashraf, Zhu, Jie, Sabharwal, Rachit, Roberts, Kirk, Cao, Tru, Wu, Hulin·2023·Knowledge and Information Systems
DOI 10.1007/s10115-023-01901-x
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Abstract

A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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Metadata

Authors
Zhang, Z., Patra, B. G., Yaseen, A., Zhu, J., Sabharwal, R., Roberts, K., Cao, T., & Wu, H.
Affiliations
  • The University of Texas Health Science Center at Houston (US)
  • Cornell University (US)
Corresponding author
  • Zhang, Zitong
  • Patra, Braja Gopal
  • Yaseen, Ashraf
  • Zhu, Jie
  • Sabharwal, Rachit
  • Roberts, Kirk
  • Cao, Tru
  • Wu, Hulin
ORCID
Venue
Knowledge and Information Systems
Publisher
Springer Science and Business Media LLC
Published
2023-06-04
Volume
65
Issue
11
Pages
4433-4478
ISSN
  • 0219-1377 (ISSN-L)
  • 0219-3116
DOI
10.1007/s10115-023-01901-x
OpenAlex
W4379283754
Cited by
73
References
0
Citations by year
2026: 21 · 2025: 33 · 2024: 15 · 2023: 4
FWCI
17.14
Type
journal article
Language
en
Open access
Licence
Locations
Knowledge and Information Systems (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
Keywords
literature recommendation, reviewer recommendation, conference recommendations, journal recommendation, content-based filtering, collaborative filtering, user feedback

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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